




"""simple docstring"""

import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__magic_name__				     =     {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}




def     _A					( __lowercase    ):





       """simple docstring"""



       assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
       assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
       if args.mlm:
              assert os.path.isfile(args.token_counts    )
              assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
       else:
              assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

       assert args.teacher_type == args.student_type or (
           args.student_type == "distilbert" and args.teacher_type == "bert"
       )
       assert os.path.isfile(args.student_config    )
       if args.student_pretrained_weights is not None:
              assert os.path.isfile(args.student_pretrained_weights    )

       if args.freeze_token_type_embds:
              assert args.student_type in ["roberta"]

       assert args.alpha_ce >= 0.0
       assert args.alpha_mlm >= 0.0
       assert args.alpha_clm >= 0.0
       assert args.alpha_mse >= 0.0
       assert args.alpha_cos >= 0.0
       assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0




def     _A					( __lowercase							,     __lowercase    ):





       """simple docstring"""



       if args.student_type == "roberta":
              lowerCamelCase__          =						False
       elif args.student_type == "gpt2":
              lowerCamelCase__          =						False




def     _A					( __lowercase							,     __lowercase    ):





       """simple docstring"""



       if args.student_type == "roberta":
              lowerCamelCase__          =						False




def     _A					( ):





       """simple docstring"""



       lowerCamelCase__          =						argparse.ArgumentParser(description="""Training"""    )
       parser.add_argument("""--force"""							,     action="""store_true"""							,     help="""Overwrite dump_path if it already exists."""    )

       parser.add_argument(
           """--dump_path"""							,     type=a__							,     required=a__							,     help="""The output directory (log, checkpoints, parameters, etc.)"""    )
       parser.add_argument(
           """--data_file"""							,     type=a__							,     required=a__							,     help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""							,     )

       parser.add_argument(
           """--student_type"""							,     type=a__							,     choices=["""distilbert""", """roberta""", """gpt2"""]							,     required=a__							,     help="""The student type (DistilBERT, RoBERTa)."""							,     )
       parser.add_argument("""--student_config"""							,     type=a__							,     required=a__							,     help="""Path to the student configuration."""    )
       parser.add_argument(
           """--student_pretrained_weights"""							,     default=a__							,     type=a__							,     help="""Load student initialization checkpoint."""    )

       parser.add_argument(
           """--teacher_type"""							,     choices=["""bert""", """roberta""", """gpt2"""]							,     required=a__							,     help="""Teacher type (BERT, RoBERTa)."""    )
       parser.add_argument("""--teacher_name"""							,     type=a__							,     required=a__							,     help="""The teacher model."""    )

       parser.add_argument("""--temperature"""							,     default=2.0							,     type=a__							,     help="""Temperature for the softmax temperature."""    )
       parser.add_argument(
           """--alpha_ce"""							,     default=0.5							,     type=a__							,     help="""Linear weight for the distillation loss. Must be >=0."""    )
       parser.add_argument(
           """--alpha_mlm"""							,     default=0.0							,     type=a__							,     help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""							,     )
       parser.add_argument("""--alpha_clm"""							,     default=0.5							,     type=a__							,     help="""Linear weight for the CLM loss. Must be >=0."""    )
       parser.add_argument("""--alpha_mse"""							,     default=0.0							,     type=a__							,     help="""Linear weight of the MSE loss. Must be >=0."""    )
       parser.add_argument(
           """--alpha_cos"""							,     default=0.0							,     type=a__							,     help="""Linear weight of the cosine embedding loss. Must be >=0."""    )

       parser.add_argument(
           """--mlm"""							,     action="""store_true"""							,     help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""    )
       parser.add_argument(
           """--mlm_mask_prop"""							,     default=0.15							,     type=a__							,     help="""Proportion of tokens for which we need to make a prediction."""							,     )
       parser.add_argument("""--word_mask"""							,     default=0.8							,     type=a__							,     help="""Proportion of tokens to mask out."""    )
       parser.add_argument("""--word_keep"""							,     default=0.1							,     type=a__							,     help="""Proportion of tokens to keep."""    )
       parser.add_argument("""--word_rand"""							,     default=0.1							,     type=a__							,     help="""Proportion of tokens to randomly replace."""    )
       parser.add_argument(
           """--mlm_smoothing"""							,     default=0.7							,     type=a__							,     help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""							,     )
       parser.add_argument("""--token_counts"""							,     type=a__							,     help="""The token counts in the data_file for MLM."""    )

       parser.add_argument(
           """--restrict_ce_to_mask"""							,     action="""store_true"""							,     help="""If true, compute the distillation loss only the [MLM] prediction distribution."""							,     )
       parser.add_argument(
           """--freeze_pos_embs"""							,     action="""store_true"""							,     help="""Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only."""							,     )
       parser.add_argument(
           """--freeze_token_type_embds"""							,     action="""store_true"""							,     help="""Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only."""							,     )

       parser.add_argument("""--n_epoch"""							,     type=a__							,     default=3							,     help="""Number of pass on the whole dataset."""    )
       parser.add_argument("""--batch_size"""							,     type=a__							,     default=5							,     help="""Batch size (for each process)."""    )
       parser.add_argument(
           """--group_by_size"""							,     action="""store_false"""							,     help="""If true, group sequences that have similar length into the same batch. Default is true."""							,     )

       parser.add_argument(
           """--gradient_accumulation_steps"""							,     type=a__							,     default=50							,     help="""Gradient accumulation for larger training batches."""							,     )
       parser.add_argument("""--warmup_prop"""							,     default=0.05							,     type=a__							,     help="""Linear warmup proportion."""    )
       parser.add_argument("""--weight_decay"""							,     default=0.0							,     type=a__							,     help="""Weight decay if we apply some."""    )
       parser.add_argument("""--learning_rate"""							,     default=5e-4							,     type=a__							,     help="""The initial learning rate for Adam."""    )
       parser.add_argument("""--adam_epsilon"""							,     default=1e-6							,     type=a__							,     help="""Epsilon for Adam optimizer."""    )
       parser.add_argument("""--max_grad_norm"""							,     default=5.0							,     type=a__							,     help="""Max gradient norm."""    )
       parser.add_argument("""--initializer_range"""							,     default=0.02							,     type=a__							,     help="""Random initialization range."""    )

       parser.add_argument(
           """--fp16"""							,     action="""store_true"""							,     help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""							,     )
       parser.add_argument(
           """--fp16_opt_level"""							,     type=a__							,     default="""O1"""							,     help=(
               """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."""
               """See details at https://nvidia.github.io/apex/amp.html"""
           )							,     )
       parser.add_argument("""--n_gpu"""							,     type=a__							,     default=1							,     help="""Number of GPUs in the node."""    )
       parser.add_argument("""--local_rank"""							,     type=a__							,     default=-1							,     help="""Distributed training - Local rank"""    )
       parser.add_argument("""--seed"""							,     type=a__							,     default=56							,     help="""Random seed"""    )

       parser.add_argument("""--log_interval"""							,     type=a__							,     default=500							,     help="""Tensorboard logging interval."""    )
       parser.add_argument("""--checkpoint_interval"""							,     type=a__							,     default=4000							,     help="""Checkpoint interval."""    )
       lowerCamelCase__          =						parser.parse_args()
       sanity_checks(a__    )

       # ARGS #
       init_gpu_params(a__    )
       set_seed(a__    )
       if args.is_master:
              if os.path.exists(args.dump_path    ):
                     if not args.force:
                            raise ValueError(
                                f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
                                """ itUse `--force` if you want to overwrite it"""    )
                     else:
                            shutil.rmtree(args.dump_path    )

              if not os.path.exists(args.dump_path    ):
                     os.makedirs(args.dump_path    )
              logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""    )

              # SAVE PARAMS #
              logger.info(f"""Param: {args}"""    )
              with open(os.path.join(args.dump_path							,     """parameters.json"""    )							,     """w"""    ) as f:
                     json.dump(vars(a__    )							,     a__							,     indent=4    )
              git_log(args.dump_path    )

       lowerCamelCase__          =						MODEL_CLASSES[args.student_type]
       lowerCamelCase__          =						MODEL_CLASSES[args.teacher_type]

       # TOKENIZER #
       lowerCamelCase__          =						teacher_tokenizer_class.from_pretrained(args.teacher_name    )
       lowerCamelCase__          =						{}
       for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
              lowerCamelCase__          =						tokenizer.all_special_tokens.index(a__    )
              lowerCamelCase__          =						tokenizer.all_special_ids[idx]
       logger.info(f"""Special tokens {special_tok_ids}"""    )
       lowerCamelCase__          =						special_tok_ids
       lowerCamelCase__          =						tokenizer.max_model_input_sizes[args.teacher_name]

       # DATA LOADER #
       logger.info(f"""Loading data from {args.data_file}"""    )
       with open(args.data_file							,     """rb"""    ) as fp:
              lowerCamelCase__          =						pickle.load(a__    )

       if args.mlm:
              logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""    )
              with open(args.token_counts							,     """rb"""    ) as fp:
                     lowerCamelCase__          =						pickle.load(a__    )

              lowerCamelCase__          =						np.maximum(a__							,     1    ) ** -args.mlm_smoothing
              for idx in special_tok_ids.values():
                     lowerCamelCase__          =						0.0  # do not predict special tokens
              lowerCamelCase__          =						torch.from_numpy(a__    )
       else:
              lowerCamelCase__          =						None

       lowerCamelCase__          =						LmSeqsDataset(params=a__							,     data=a__    )
       logger.info("""Data loader created."""    )

       # STUDENT #
       logger.info(f"""Loading student config from {args.student_config}"""    )
       lowerCamelCase__          =						student_config_class.from_pretrained(args.student_config    )
       lowerCamelCase__          =						True

       if args.student_pretrained_weights is not None:
              logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""    )
              lowerCamelCase__          =						student_model_class.from_pretrained(args.student_pretrained_weights							,     config=a__    )
       else:
              lowerCamelCase__          =						student_model_class(a__    )

       if args.n_gpu > 0:
              student.to(f"""cuda:{args.local_rank}"""    )
       logger.info("""Student loaded."""    )

       # TEACHER #
       lowerCamelCase__          =						teacher_model_class.from_pretrained(args.teacher_name							,     output_hidden_states=a__    )
       if args.n_gpu > 0:
              teacher.to(f"""cuda:{args.local_rank}"""    )
       logger.info(f"""Teacher loaded from {args.teacher_name}."""    )

       # FREEZING #
       if args.freeze_pos_embs:
              freeze_pos_embeddings(a__							,     a__    )
       if args.freeze_token_type_embds:
              freeze_token_type_embeddings(a__							,     a__    )

       # SANITY CHECKS #
       assert student.config.vocab_size == teacher.config.vocab_size
       assert student.config.hidden_size == teacher.config.hidden_size
       assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
       if args.mlm:
              assert token_probs.size(0    ) == stu_architecture_config.vocab_size

       # DISTILLER #
       torch.cuda.empty_cache()
       lowerCamelCase__          =						Distiller(
           params=a__							,     dataset=a__							,     token_probs=a__							,     student=a__							,     teacher=a__    )
       distiller.train()
       logger.info("""Let\'s go get some drinks."""    )


if __name__ == "__main__":
  main()














"""simple docstring"""


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	List[Any]     , *UpperCAmelCase							:	Any     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
											requires_backends(self     , 'vision'   )
											self.check_model_type(
											    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	List[str]=None   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			{}
											lowerCAmelCase   :int						=			{}

											if prompt is not None:
																	lowerCAmelCase   :Any						=			prompt

											if generate_kwargs is not None:
																	lowerCAmelCase   :List[Any]						=			generate_kwargs
											if max_new_tokens is not None:
																	if "generate_kwargs" not in forward_kwargs:
																							lowerCAmelCase   :Any						=			{}
																	if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																							raise ValueError(
																							    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
																							    ' please use only one'   )
																	lowerCAmelCase   :Union[str, Any]						=			max_new_tokens
											return preprocess_params, forward_kwargs, {}
					def __call__(							self							:	Tuple     , UpperCAmelCase							:	Union[str, List[str], "Image.Image", List["Image.Image"]]     , **UpperCAmelCase							:	Any   )	->       Optional[int]:
											return super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Any=None   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(UpperCAmelCase   )

											if prompt is not None:
																	if not isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																							raise ValueError(
																							    f"""Received an invalid text input, got - {type(UpperCAmelCase   )} - but expected a single string. """
																							    'Note also that one single text can be provided for conditional image to text generation.'   )

																	lowerCAmelCase   :List[str]						=			self.model.config.model_type

																	if model_type == "git":
																							lowerCAmelCase   :Any						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Union[str, Any]						=			self.tokenizer(text=UpperCAmelCase     , add_special_tokens=UpperCAmelCase   ).input_ids
																							lowerCAmelCase   :Optional[int]						=			[self.tokenizer.cls_token_id] + input_ids
																							lowerCAmelCase   :Union[str, Any]						=			torch.tensor(UpperCAmelCase   ).unsqueeze(0   )
																							model_inputs.update({'input_ids': input_ids}   )

																	elif model_type == "pix2struct":
																							lowerCAmelCase   :Optional[Any]						=			self.image_processor(images=UpperCAmelCase     , header_text=UpperCAmelCase     , return_tensors=self.framework   )

																	elif model_type != "vision-encoder-decoder":
																							# vision-encoder-decoder does not support conditional generation
																							lowerCAmelCase   :int						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Optional[Any]						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																							model_inputs.update(UpperCAmelCase   )

																	else:
																							raise ValueError(f"""Model type {model_type} does not support conditional text generation"""   )

											else:
																	lowerCAmelCase   :str						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )

											if self.model.config.model_type == "git" and prompt is None:
																	lowerCAmelCase   :Union[str, Any]						=			None

											return model_inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Any=None   )	->       int:
											# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
											# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
											if (
											    "input_ids" in model_inputs
											    and isinstance(model_inputs['input_ids']     , UpperCAmelCase   )
											    and all(x is None for x in model_inputs['input_ids']   )
											):
																	lowerCAmelCase   :Optional[int]						=			None

											if generate_kwargs is None:
																	lowerCAmelCase   :Optional[int]						=			{}
											# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
											#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
											#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
											#  in the `_prepare_model_inputs` method.
											lowerCAmelCase   :Optional[int]						=			model_inputs.pop(self.model.main_input_name   )
											lowerCAmelCase   :Any						=			self.model.generate(UpperCAmelCase     , **UpperCAmelCase     , **UpperCAmelCase   )
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	Union[str, Any]   )	->       Any:
											lowerCAmelCase   :str						=			[]
											for output_ids in model_outputs:
																	lowerCAmelCase   :Optional[int]						=			{
																	    'generated_text': self.tokenizer.decode(
																	        UpperCAmelCase     , skip_special_tokens=UpperCAmelCase     , )
																	}
																	records.append(UpperCAmelCase   )
											return records